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2023 | OriginalPaper | Chapter

Machine Learning Techniques for Aspect Analysis of Employee Attrition

Authors : Anamika Hooda, Purva Garg, Nonita Sharma, Monika Mangla

Published in: Intelligent Systems and Machine Learning

Publisher: Springer Nature Switzerland

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Abstract

Employee attrition is the reduction in the employee workforce, which can be defined as the rate of employees leaving the company faster than the rate they are hired. Attrition may be for the whole establishment but sometimes it might be particular for a business field. This happens when there is intervention of technology that contribute in replacing the human workforce. There are several factors contributing to employee attrition, a few being age, number of years in the company, manager, technology change, etc. It is vital to understand the impact of these factors on employee attrition so that necessary action can be taken to avoid this. Thus, Machine learning technique is being used nowadays to inspect and predict the data of several real-life applications. After employing the models, authors performed the analysis on each of them using confusion matrix, F-1 score, recall, precision, etc., and found that the best model is SVM with an accuracy of 85.60%.

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Metadata
Title
Machine Learning Techniques for Aspect Analysis of Employee Attrition
Authors
Anamika Hooda
Purva Garg
Nonita Sharma
Monika Mangla
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-35081-8_23

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